SoroushVahidi/scidocs huggingface dataset:
WhyCon : An Efficent , Marker-based Localization System
(2015)

We present an open-source marker-based localization system intended as a low-cost easy-to-deploy solution for aerial and swarm robotics. The main advantage of the presented method is its high computational efficiency, which allows its deployment on small robots with limited computational resources. Even on low-end computers, the core component of the system can detect and estimate 3D positions of hundreds of black and white markers at the maximum frame-rate of standard cameras. The method is robust to changing lighting conditions and achieves accuracy in the order of millimeters to centimeters. Due to its reliability, simplicity of use and availability as an open-source ROS module (http://purl.org/robotics/whycon), the system is now used in a number of aerial robotics projects where fast and precise relative localization is required.
A Novel Feature Selection Based on One-Way ANOVA F-Test for E-Mail Spam Classification
(2014)

Spam is commonly defined as unwanted e-mails and it became a global threat against e-mail users. Although, Support Vector Machine (SVM) has been commonly used in e-mail spam classification, yet the problem of high data dimensionality of the feature space due to the massive number of e-mail dataset and features still exist. To improve the limitation of SVM, reduce the computational complexity (efficiency) and enhancing the classification accuracy (effectiveness). In this study, feature selection based on one-way ANOVA F-test statistics scheme was applied to determine the most important features contributing to e-mail spam classification. This feature selection based on one-way ANOVA F-test is used to reduce the high data dimensionality of the feature space before the classification process. The experiment of the proposed scheme was carried out using spam base wellknown benchmarking dataset to evaluate the feasibility of the proposed method. The comparison is achieved for different datasets, categorization algorithm and success measures. In addition, experimental results on spam base English datasets showed that the enhanced SVM (FSSVM) significantly outperforms SVM and many other recent spam classification methods for English dataset in terms of computational complexity and dimension reduction.
SEISMIC BEHAVIOR OF INTERMEDIATE BEAMS IN STEEL PLATE SHEAR WALLS
(2006)

This paper presents some preliminary results of an ongoing research whose objective is to investigate the seismic behavior of intermediate beams (i.e., the beams other than those at the roof and foundation levels) in multi-story steel plate shear walls (SPSWs). Of primary interest is the determination of the strength level needed to avoid the formation of in-span plastic hinges, a relevant practical issue that has not been considered in past investigations. To attain this objective, the seismic response of different SPSW models was analyzed by performing linear and nonlinear analysis. The intermediate beams of the SPSW models were designed to resist: (I) forces imposed by gravity loads only; (II) forces determined by the ASCE 7 load combinations; and (III) forces imposed by fully yielded plates. For comparison purposes, SPSW models designed according to the Canadian Standard CAN/CSA S16-01 were considered as well. It is concluded that intermediate beams designed according to criteria I and II are prone to inspan plastic hinges and to excessive plastic deformations. It was also found that, while in-span plastic hinges do not appear in the intermediate beams of the CAN/CSA S16-01 models, they do appear in the roof and foundation beams of these models when a collapse mechanism develops.
MBBA: A Multi-Bandwidth Bus Arbiter for Hard Real-Time
(2010)

Multi-core architectures are being increasingly used in embedded systems as they offer several advantages: improved hardware integration, low thermal dissipation and reduced energy consumption, while they make it possible to improve the computing power. In order to run real-time software on a multicore architecture, computing the Worst-Case Execution Time of every thread should be achievable. This notably involves bounding memory latencies by employing a predictable bus arbiter. However, state-of-the-art techniques prove to be irrelevant to schedule unbalanced workloads in which some threads require more bus bandwidth than the other ones. This paper proposes a new bus arbitration scheme that ensures that the shared bus latencies can be upper bounded. Compared to other schemes that make the bus latencies predictable, like the Round-Robin protocol, our approach defines several levels of bandwidth to meet requirements that may vary from one thread to another. Experimental results (WCET estimates) show that the worst-case bus latency is noticeably shortened, compared to Round-Robin, for the cores with highest priority that get the largest bandwidth. The relevance of the scheme is shown through an example workload composed of various benchmarks.
LoRa Scalability: A Simulation Model Based on Interference Measurements
(2017)

LoRa is a long-range, low power, low bit rate and single-hop wireless communication technology. It is intended to be used in Internet of Things (IoT) applications involving battery-powered devices with low throughput requirements. A LoRaWAN network consists of multiple end nodes that communicate with one or more gateways. These gateways act like a transparent bridge towards a common network server. The amount of end devices and their throughput requirements will have an impact on the performance of the LoRaWAN network. This study investigates the scalability in terms of the number of end devices per gateway of single-gateway LoRaWAN deployments. First, we determine the intra-technology interference behavior with two physical end nodes, by checking the impact of an interfering node on a transmitting node. Measurements show that even under concurrent transmission, one of the packets can be received under certain conditions. Based on these measurements, we create a simulation model for assessing the scalability of a single gateway LoRaWAN network. We show that when the number of nodes increases up to 1000 per gateway, the losses will be up to 32%. In such a case, pure Aloha will have around 90% losses. However, when the duty cycle of the application layer becomes lower than the allowed radio duty cycle of 1%, losses will be even lower. We also show network scalability simulation results for some IoT use cases based on real data.
Improvements in muscle symmetry in children with cerebral palsy after equine-assisted therapy (hippotherapy).
(2003)

OBJECTIVE
To evaluate the effect of hippotherapy (physical therapy utilizing the movement of a horse) on muscle activity in children with spastic cerebral palsy.


DESIGN
Pretest/post-test control group.


SETTING/LOCATION
Therapeutic Riding of Tucson (TROT), Tucson, AZ.


SUBJECTS
Fifteen (15) children ranging from 4 to 12 years of age diagnosed with spastic cerebral palsy.


INTERVENTIONS
Children meeting inclusion criteria were randomized to either 8 minutes of hippotherapy or 8 minutes astride a stationary barrel.


OUTCOME MEASURES
Remote surface electromyography (EMG) was used to measure muscle activity of the trunk and upper legs during sitting, standing, and walking tasks before and after each intervention.


RESULTS
After hippotherapy, significant improvement in symmetry of muscle activity was noted in those muscle groups displaying the highest asymmetry prior to hippotherapy. No significant change was noted after sitting astride a barrel.


CONCLUSIONS
Eight minutes of hippotherapy, but not stationary sitting astride a barrel, resulted in improved symmetry in muscle activity in children with spastic cerebral palsy. These results suggest that the movement of the horse rather than passive stretching accounts for the measured improvements.
Gain and bandwidth improvement of microstrip antenna using RIS and FPC resonator
(2017)

Improvement in gain as well as bandwidth of microstrip antenna using reactive impedance surface and Fabry Perot cavity resonator is proposed. Suspended microstrip antenna (MSA) is designed on reactive impedance surface (RIS) backed FR4 substrate. RIS improves bandwidth, gain and efficiency of antenna due to reduced coupling between MSA and ground plane. This MSA backed RIS is placed in a FPC resonator for further improving the gain of MSA. 1×1 to 4×4 square patch arrays are designed on a dielectric layer which is energized by the radiations from RIS backed MSA. The PRS layer is placed at about λ0/2 from ground. The S11 is < −9.5 dB over 5.725–6.4 GHz. Maximum gain of 15.8 dBi with gain variation < 2 dB is obtained using 4×4 array. Beside this, cross polarization and Side lobe level are < −2ο dB. Antenna offers front to back lobe ratio (FBR) > 20 dB and > 70 % antenna efficiency. The measured results of prototype structure are in agreement with the simulation results.
Using Spoken Language Benchmarks to Characterize the Expressive Language Skills of Young Children With Autism Spectrum Disorders.
(2015)

PURPOSE
Spoken language benchmarks proposed by Tager-Flusberg et al. (2009) were used to characterize communication profiles of toddlers with autism spectrum disorders and to investigate if there were differences in variables hypothesized to influence language development at different benchmark levels.


METHOD
The communication abilities of a large sample of toddlers with autism spectrum disorders (N = 105) were characterized in terms of spoken language benchmarks. The toddlers were grouped according to these benchmarks to investigate whether there were differences in selected variables across benchmark groups at a mean age of 2.5 years.


RESULTS
The majority of children in the sample presented with uneven communication profiles with relative strengths in phonology and significant weaknesses in pragmatics. When children were grouped according to one expressive language domain, across-group differences were observed in response to joint attention and gestures but not cognition or restricted and repetitive behaviors.


CONCLUSION
The spoken language benchmarks are useful for characterizing early communication profiles and investigating features that influence expressive language growth.
THE DEMOCRATIZATION OF PERSONAL CONSUMER LOANS? DETERMINANTS OF SUCCESS IN ONLINE PEER-TO-PEER LENDING COMMUNITIES
(2008)

Online peer-to-peer (P2P) lending communities enable individual consumers to borrow from, and lend money to, one another directly. We study the borrowerand loan listing-related determinants of funding success in an online P2P lending community by conceptualizing loan decision variables (loan amount, interest rate offered, duration of loan listing) as mediators between borrower attributes such as demographic characteristics, financial strength, and effort prior to making the request, and the likelihood of funding success. Borrower attributes are also treated as moderators of the effects of loan decision variables on funding success. The results of our empirical study, conducted using a database of 5,370 completed P2P loan transactions, provide support for the proposed conceptual framework. Although demographic attributes such as race and gender do affect likelihood of funding success, their effects are very small in comparison to effects of borrowers’ financial strength and their effort when listing and publicizing the loan. These results are substantially different from the documented discriminatory practices of US financial institutions, suggesting that individual lenders lend more fairly when their own investment money is at stake in P2P loans. The paper concludes with specific suggestions to borrowers to increase their chances of receiving funding in P2P lending communities, and a discussion of future research opportunities.
Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis
(2017)

The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10-13% and 3-5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.
Clash of the Contagions: Cooperation and Competition in Information Diffusion
(2012)

In networks, contagions such as information, purchasing behaviors, and diseases, spread and diffuse from node to node over the edges of the network. Moreover, in real-world scenarios multiple contagions spread through the network simultaneously. These contagions not only propagate at the same time but they also interact and compete with each other as they spread over the network. While traditional empirical studies and models of diffusion consider individual contagions as independent and thus spreading in isolation, we study how different contagions interact with each other as they spread through the network. We develop a statistical model that allows for competition as well as cooperation of different contagions in information diffusion. Competing contagions decrease each other's probability of spreading, while cooperating contagions help each other in being adopted throughout the network. We evaluate our model on 18,000 contagions simultaneously spreading through the Twitter network. Our model learns how different contagions interact with each other and then uses these interactions to more accurately predict the diffusion of a contagion through the network. Moreover, the model also provides a compelling hypothesis for the principles that govern content interaction in information diffusion. Most importantly, we find very strong effects of interactions between contagions. Interactions cause a relative change in the spreading probability of a contagion by 71% on the average.
Patterns of temporal variation in online media
(2011)

Online content exhibits rich temporal dynamics, and diverse realtime user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored.
 We study temporal patterns associated with online content and how the content's popularity grows and fades over time. The attention that content receives on the Web varies depending on many factors and occurs on very different time scales and at different resolutions. In order to uncover the temporal dynamics of online content we formulate a time series clustering problem using a similarity metric that is invariant to scaling and shifting. We develop the K-Spectral Centroid (K-SC) clustering algorithm that effectively finds cluster centroids with our similarity measure. By applying an adaptive wavelet-based incremental approach to clustering, we scale K-SC to large data sets.
 We demonstrate our approach on two massive datasets: a set of 580 million Tweets, and a set of 170 million blog posts and news media articles. We find that K-SC outperforms the K-means clustering algorithm in finding distinct shapes of time series. Our analysis shows that there are six main temporal shapes of attention of online content. We also present a simple model that reliably predicts the shape of attention by using information about only a small number of participants. Our analyses offer insight into common temporal patterns of the content on theWeb and broaden the understanding of the dynamics of human attention.
An annotation tool for automatically triangulating individuals' psychophysiological emotional reactions to digital media stimuli
(2015)

Current affective user experience studies require both laborious and time-consuming data analysis, as well as dedicated affective classification algorithms. Moreover, the high technical complexity and lack of general guidelines for developing these affective classification algorithms further limits the comparability of the obtained results. In this paper we target this issue by presenting a tool capable of automatically annotating and triangulating players' physiologically interpreted emotional reactions to in-game events. This tool was initially motivated by an experimental psychology study regarding the emotional habituation effects of audio-visual stimuli in digital games and we expect it to contribute in future similar studies by providing both a deeper and more objective analysis on the affective aspects of user experience. We also hope it will contribute towards the rapid implementation and accessibility of this type of studies by open-sourcing it. Throughout this paper we describe the development and benefits presented by our tool, which include: enabling researchers to conduct objective a posteriori analyses without disturbing the gameplay experience, automating the annotation and emotional response identification process, and formatted data exporting for further analysis in third-party statistical software applications.
A 3-D printed lightweight cavity-backed monocone antenna
(2017)

A lightweight exponentially tapered cavity-backed mono-cone antenna made by 3-D printing and conductive paint coating is proposed. To make the 3-D printed structure be metalized, three layers of conductive paint are used. To verify the proposed fabrication method, the 3-D printed antenna was fabricated and its return loss and far-field radiation characteristics were analyzed.
Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images
(2018)

Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%–2% and help to eliminate “salt and pepper” phenomena and block effects.
Generating Neural Networks with Neural Networks
(2018)

Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation generalizes variational inference. We use multi-layered perceptrons to form the mapping from the low dimensional input random vector to the high dimensional weight space, and demonstrate how to reduce the number of parameters in this mapping by parameter sharing. We perform experiments and show that the generated weights are diverse and lie on a non-trivial manifold.
OPTIMIZING THE PERFORMANCE OF VEHICLE-TO-GRID ( V 2 G ) ENABLED BATTERY ELECTRIC VEHICLES THROUGH A SMART CHARGE SCHEDULING MODEL
(2015)

−A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV
Reading Between the Lines: Overcoming Data Sparsity for Accurate Classification of Lexical Relationships
(2015)

The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall.
MC-HOG Correlation Tracking with Saliency Proposal
(2016)

Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.